Method for compressing wideband sensor signal

ABSTRACT

Example implementations described herein are directed to systems and methods for extracting signal in the presence of noise for industrial IoT systems. Through the example implementations described herein, the high-frequency band signal of the sensor output can be maintained despite data compression, while also retaining information regarding the condition of the machine. Example implementations can also generate compressed data pairs to synchronized downsampled envelopes and spectrum data.

BACKGROUND Field

The present disclosure is generally directed to Internet of Things (IoT)systems, and more specifically, to wideband sensor signal compressionprocessing techniques.

Related Art

In the related art, there are implementations for transforming machinemaintenance from human know-how-based schemes into IoT systems.Extracting signal that reflects a usage or a condition of a machinethrough sensors is utilized for such maintenance applications. However,in many cases, legacy machines that are already working in a productionline are not equipped with such sensors since they were not designed tobe integrated into the IoT system. Thus, such legacy machines rely onmanual maintenance to maintain operations.

One problem regarding the design for IoT systems is the limitation ofresources. To extract the machine information from the sensor signal(e.g., vibration signal, acoustic signal), a wideband sensor and dataacquisition system (e.g. more than 10 kHz) is needed. This is becausephysical signals from a measurement target (e.g., such as a bearing or acutting tool) tend to be modulated at a natural frequency of othermechanical parts of the machine. Thus, the signal frequency band may goto another frequency band which is higher than the original frequencyband of the signal. Therefore, you will need a high sampling rate,practically more than 20-kHz, to capture such the modulated signalwithout aliasing that degrades the signal-to-ratio of the data.

In addition, it is known in related art implementations that monitoringa signal in the high frequency band is a method to detect a potentialphysical failure as early as possible. The reason for this is that theearly phases of a mechanical failure develop high-frequency vibration orsound such as acoustic emission.

However, wideband monitoring (e.g., from DC to high frequency), willrequire abundant network capacity, storage capacity, and computerprocessing resources. As there is always a demand for cost reduction ofIoT systems, reducing the requirements for computing resources whilemaintaining the amount of data and bandwidth, is the one major problemthat should be resolved in the design process of an IoT system.

In the related art, there is a method that compares a current signalspectrum and a reference signal spectrum to detect a failure wasdisclosed. Further, there are related art systems and methods to extractenvelopes of signals and applying down-sampling in order to reduce theamount and complexity of signal.

SUMMARY

Aspects of the present disclosure can include a method for managingsensor data from a machine having a plurality of machine states, themethod involving decomposing sensor data into a plurality ofband-limited data to generate a decomposed sensor data; and executingdownsampling on each of the decomposed sensor data to generatedownsampled decomposed sensor data; wherein the downsampling cycle isshorter than a duration of the each of the machine states.

Aspects of the present disclosure can include a non-transitory computerreadable medium, storing instructions for managing sensor data from amachine having a plurality of machine states, the instructions involvingdecomposing sensor data into a plurality of band-limited data togenerate a decomposed sensor data; and executing downsampling on each ofthe decomposed sensor data to generate downsampled decomposed sensordata; wherein the downsampling cycle is shorter than a duration of theeach of the machine states.

Aspects of the present disclosure can include a system for managingsensor data from a machine having a plurality of machine states, thesystem involving means for decomposing sensor data into a plurality ofband-limited data to generate a decomposed sensor data; and means forexecuting downsampling on each of the decomposed sensor data to generatedownsampled decomposed sensor data; wherein the downsampling cycle isshorter than a duration of the each of the machine states.

Aspects of the present disclosure can include an apparatus for managingsensor data from a machine having a plurality of machine states, theapparatus involving a processor configured to decompose sensor data intoa plurality of band-limited data to generate a decomposed sensor data;and execute downsampling on each of the decomposed sensor data togenerate downsampled decomposed sensor data; wherein the downsamplingcycle is shorter than a duration of the each of the machine states. Theapparatus may facilitate a common enclosure for a processor integratedwith a sensor configured to provide the sensor data.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example block diagram of a local computer in apart of an IoT system with the signal processing method in accordancewith an example implementation.

FIG. 2 illustrates an example of raw signal developed by a rotationalmachine, in accordance with an example implementation.

FIG. 3 illustrates an example block diagram for data compression inaccordance with an example implementation.

FIG. 4 illustrates an example expression of data processing at eachprocessing block, in accordance with an example implementation.

FIG. 5 illustrates an example flow chart of the signal analysis block,in accordance with an example implementation.

FIG. 6 illustrates an example block diagram of a local computer inaccordance with another example implementation.

FIG. 7 illustrates an example block diagram of a local computer inaccordance with another example implementation.

FIG. 8 illustrates an example flow chart of the IoT system analysis, inaccordance with an example implementation.

FIG. 9 illustrates an example computing environment with an examplecomputer device suitable for use in some example implementations.

DETAILED DESCRIPTION

The following detailed description provides details of the figures andexample implementations of the present application. Reference numeralsand descriptions of redundant elements between figures are omitted forclarity. Terms used throughout the description are provided as examplesand are not intended to be limiting. For example, the use of the term“automatic” may involve fully automatic or semi-automaticimplementations involving user or administrator control over certainaspects of the implementation, depending on the desired implementationof one of ordinary skill in the art practicing implementations of thepresent application. Selection can be conducted by a user through a userinterface or other input means, or can be implemented through a desiredalgorithm. Example implementations as described herein can be utilizedeither singularly or in combination and the functionality of the exampleimplementations can be implemented through any means according to thedesired implementations.

Example implementations described herein involve a signal processingmethod designed to reduce the requirement for computing resources whilemaintaining the amount of signal, especially for machine maintenanceapplications.

As will be described herein, signal refers to a physical quantity thatis useful for an application. Natural phenomena such as vibration oracoustic signal can be referred to generally as signals. Voltage,current, or digitized values that are inside a sensor or a computer canbe also referred to as signals.

As will be described herein, noise refers to physical quantities thatare not useful for the application. In other words, they are generatedfrom signal sources which are not of interest to the application. Forexample, when an application monitors the condition of a bearing,vibration caused by a cooling fan is “noise.” Random noise such asthermal noise or quantization noise can be also referred as noise.Voltage, current, or digitized value that are inside a sensor or acomputer can be also referred as noise.

As will be described herein, data refers to the combined physicalquantities of signal and noise. Raw data is the data before applying anysignal processing except for digitization. Signal processing does notonly refer to processing of signal, but can also refer to processing ofdata.

In a first example implementation, there is a signal processing methodfor extracting and compressing information from a wideband sensorsignal, in order to monitor a usage or a condition of a target whichincurs vibration according to its usage or condition. In this exampleimplementation, it is assumed that a monitoring target, such as abearing or a cutting tool, develops higher vibration magnitude as theusage increases or condition degrades. It is also assumed that thedegree of increasing vibration is monotonic.

FIG. 1 illustrates an example block diagram of a local computer 101 in apart of an IoT system with the signal processing method in accordancewith an example implementation. In this example, the local computer isconnected to at least one sensor 102 and a data acquisition device (DAQ)103 through a wideband data transmission path 104. The wideband datatransmission path 104 should have more bandwidth than the bandwidth ofthe sensor 102, so that the sensor 102 can transmit its wideband dataoutput.

The sensor 102 is attached at the surface of the enclosure of a machine,such as a pump or a lathe machine. The reason for not installing thesensor at the target itself is that many targets, such as a bearing or acutting tool, are equipped in a machine. Thus, installing a sensorinside the enclosure of the machine may cause a degradation of aproduction quality. Such degradation can be a problem if a productionline involves the machine.

In this example, the sensor 102 is a vibration sensor (accelerometer).However, other sensors, such as a microphone, an ultrasonic sensor, acurrent sensor, a temperature sensor, and so on may also be utilized inaccordance with the desired implementation, and the present disclosureis not particularly limited thereto.

The DAQ 103 converts analog continuous data into discrete digital dataat a predetermined sampling rate. In an example implementation, thesampling rate is at least double the bandwidth of the sensor 102 so thatthe DAQ 103 can preserve the entire signal from the sensor 102. Thedigitized raw data is fed to two signal processing paths in theextraction block 105; envelope detector 106 and decomposer 109.

The envelope detector 106 calculates the envelope of the input data. Forexample, the envelope of the input data can be calculated throughapplying any signal processing method, such as Hilbert transform or alow pass filter following a root-mean-square function. The purpose ofcalculating an envelope is to identify the important states of themachine that should be monitored when signal is extracted from inputdata containing both signal and noise. For example, for monitoring theusage or condition of a cutting tool of an automatic lathe machine,there can be multiple states of the machine operation such as idle state(State 1), the state of feeding a work from outside of the machine intothe processing chamber of the machine (State 2), the state of placingthe work at a spindle (State 3), the state of processing the work withthe cutting tool (State 4), the state of removing the work from thespindle (State 5), and the state of ejecting the work from the machineand putting the work on a conveyer (State 6).

FIG. 2 illustrates an example of raw signal developed by a rotationalmachine, in accordance with an example implementation. In the example ofFIG. 2, to monitor the usage or condition of the cutting tool throughthe vibration developed by friction between the cutting tool and a work,example implementations can be configured to monitor only the vibrationduring Phase 4, in which the cutting tool processes a work and thereforegenerates vibration. As shown in FIG. 2, as each state has uniquevibration level and a repeating sequence. Thus, envelopes can be used toidentify each state of the machine in the further analysis.

Further, as illustrated in FIG. 2, each state of the machine is not asquick as the sampling rate of the sensor data. Therefore, once theenvelope of the input data is calculated, example implementationsconduct down-sampling to reduce the amount of data while retaining theinformation regarding the machine states. Such example implementationsreduce the computing resource requirements in the ensuing signalprocessing. In this example, the down-sampling is performed aftersummarizing, such as moving average (i.e. low pass filtering). Bothprocesses are performed in the summarize and down-sampling block 107.Then the down-sampled data will be transmitted to following signalprocessing blocks through a narrow-band (i.e. slow sampling rate)transmission path 108.

To analyze the usage or condition of the target through vibration data,wide-band data can be more informative to obtain signal indicating theusage or condition of a machine since such wide-band data contains highfrequency signal in which the modulated signal or the signal generatedby the early phase of a failure may exist. Further, wideband signaldemands processing resources of computers. However, as illustrated inFIG. 2, each machine state is not as quick as the sampling rate of thesensor data. Therefore, the example implementations compress the sensordata by utilizing such characteristics of the machine-related data. Thatis, example implementations compress wideband data in time-domain afterdecomposing wideband data into multiple band-limited data.

FIG. 3 illustrates an example block diagram for data compression inaccordance with an example implementation. The wideband raw dataprovided by the DAQ 103 is fed to following multiple filters: a low passfilter 901, a variety of band pass filters 902, and a high pass filter903. Then each band-limited data that has only its band-limitedfrequency component is fed to a following feature extraction block 110.After the feature extraction, they will be summarized along thetime-domain (e.g. by moving average) and down-sampled in summarycalculator 904 and downsampler 905, respectively. The summarize anddown-sampling block 111 can involve the summary calculator 904 and thedown-sampler 905.

Implementing a filter bank that involves the low pass filter 901, thevariety of band pass filters 902, and the high pass filter 903 is oneexample to achieve the signal decomposing. After the filtering at eachfilter of the filter bank, example implementations can calculateroot-mean-square, peak-to-peak, crest factor, or kurtosis to extractfeature values of each data band at each feature extractor 110. In thefollowing explanation, the output of feature extract 110 is referred toas “feature value.” As these filtering processes are independent eachother, example implementations can calculate the feature values inparallel to reduce the calculation time.

FFT (Fast Fourier transform) method is another example that can be usedto facilitate the signal decomposition. When FFT methods are applied,example implementations can combine the real part and the imaginary partas magnitude to get the absolute strength of the vibration. Such dataconversion may be also done in the feature extractor 110. In thefollowing example, FFT is utilized as a signal decomposition method.

If the summary calculator 904 and the down-sampler 905 are appliedbefore decomposing the raw data into multiple band-limited data,aliasing will combine all the frequency band data into one narrow band.Therefore, the signal in high-frequency band may no longer be detectablein the following signal processing (this can be referred as lowsignal-to-noise ratio). However, since the disclosed method decomposesthe data into multiple band-limited data, down-sampling at eachband-limited data will not cause serious degradation of thesignal-to-noise ratio due to aliasing due to the suppression ofout-of-band data at each band-limited data.

Finally, each down-sampled band-limited data will be bundled atmultiplexer 906. Then the bundled data will be fed to the next stepthrough the narrow-band multiplexed data path 108.

Both the envelope data and decomposed band-limited data are synchronizedand paired in the pairing block 112, as shown in FIG. 1. Then the datapair will be fed to further signal analysis block 114 through theinter-module interface 113. The advantage of the synchronization betweenthe envelope data and decomposed band-limited data is that the signalanalysis block 114 can label each decomposed band-limited data inaccordance with the labels of the envelope data. Then the signalanalysis block 114 picks out at least one data pair that corresponds tothe state in which the target generates vibration. In this example, thesignal analysis block 114 picks out only Phase 4.

Once the pairing block 112 sends the paired data to the signal analysisblock 114, the raw data with high sampling rate is no longer required.That means new raw data, as provided by sensor 102, can be stored in thesame memory area or storage area as the previous raw sensor data. Suchimplementations thereby reduce the requirement for storage capacity.

FIG. 4 illustrates an example expression of data processing at eachprocessing block, in accordance with an example implementation. In FIG.4, some qualitative charts of data in each block are shown. The raw datawith the sampling rate of 20 kHz is shown as shown in C401. Then, theenvelope of the raw signal is calculated as shown in C402. After that,that envelope data is down-sampled at 10 Hz (Sampling cycle: 0.1seconds) as shown in C403.

The decomposer 109 decomposes the raw data into multiple band-limiteddata. As FFT is applied in this example, the real and imaginary portionsof the data of each multiple band-limited data is obtained as shown inC404. The feature extractor 110 calculates the absolute amplitude (C405)and transmit it to the summarize and down-sampling block 111. Eachdecomposed band-limited data has some redundancy along the time-domaindue to the slow transitions between machine states. Therefore,compression along the time-domain within each of the machine statesafter decomposing data into multiple band-limited data will not causemuch information loss even if the signal is high frequency. In thesummarize and down-sampling block 111, the absolute amplitude of FFT issummarized by down-sampling at every 0.1 seconds as shown in C406.Finally, the paired data C407 is fed to the following signal analysisblock 114 after the synchronization with the envelope data.

Quantitatively, for example, if a sampling rate is 20 kHz and the numberof FFT points is 256, the decomposer 109 creates 128 band-limited data(since the number of FFT points is 256) with the bandwidth of (20000Hz/128=156.25 Hz) from DC to 10 kHz. Therefore, the first band-limiteddata may have a frequency component from DC to 156.25 Hz, the second onemay have 156.25 Hz to 312.5 Hz, and the last one has from 9843.75 Hz to10 kHz. Regarding the down-sampling cycle for compression, as shown inFIG. 2, each state lasts at least 250 milliseconds in this example.Therefore, the down-sampling cycle for each band-limited data may be setto a number up to every 250 milliseconds for data compression. Bysetting the down-sampling cycle to be less than the duration of each ofthe expected machine states but more than the original sampling cycle,the amount of data can be decreased while maintaining the informationregarding the target machine's information of each states. Alternativelythe signal analysis block 114 could analyze a significant change in theenvelope to find the change of the states and set a value less than theminimum duration among each change as the down-sampling cycle to thedecomposer 109.

FIG. 5 illustrates an example flow chart of the signal analysis block114, in accordance with an example implementation. Firstly, in S601, thesignal analysis block 114 starts receiving data pairs until apredetermined condition is met (e.g., a predetermined amount of data isstored, a certain period of time has elapsed, and so on in accordancewith the desired implementation). More specifically, S601 collects datapairs with sampling rate of Δt until the sum of the data length reachest calibration (S602).

Then, at S603, the signal analysis block 114 performs the iteratingpattern identification to identify and categorize the states of amachine for the monitoring target. If the machine has only binary states(e.g., a simple rotational machine which has only “on” and “off.”), thisprocess may be skipped. More specifically, S603 calculatesautocorrelation of the envelope to identify an iterating pattern. As thetarget machine iterates the same manufacturing process, it is expectedthat the machine spends the same time for each single manufacturingprocess. Therefore, autocorrelation of the envelope data will reveal thecycle, denoted as T seconds, of a single manufacturing process. Then,the signal analysis block 114 finds one T-second representative waveformpattern that appears repeatedly in the envelope data. This pattern isreferred to as a “template.” This process is realized by, for example,picking out a T-second envelope data that has relatively small amplitudein the beginning and the ending part of the T seconds data while showingrelatively large amplitude in the middle of the envelope data. Anexample of such a template is illustrated in FIG. 2 Once the T secondsrepresentative pattern is identified, the signal analysis block 114extracts all the similar patterns from the envelope data set providedfrom S601 and S602. A cross-correlation calculation or Euclid distancecalculation may be applied to extract similar patterns.

At S604, the signal analysis block 114 performs a frequency bandidentification process by using each of the band-limited data featurevalues which as stored in S601 and S602. In this process, a frequencyband, in which a monotonic trend of the feature value in accordance withthe usage or condition of the machine is identified if it exists. Forexample, a curve fitting to a first-order polynomial is applied toband-limited data feature values. Then, a fitting-error calculation isperformed to extract a trend of each feature values and evaluate itsmonotonicity in accordance with the usage or condition of the machine. Afrequency band of the band-limited data with the minimum-fitting-erroris then referred to as the “signal frequency band.” Alternatively, ifthe target of a measurement is consumable items which are replacedalmost periodically, calculating autocorrelations of feature values ofeach band-limited data and extracting a frequency band which has astrong correlation around the replacement cycle of the consumable willreveal the signal frequency band.

Once the signal frequency band is identified, the measurement phase(S605, S606, and S607) is executed. The calibration phase refers to theprocess of S601, S602, S603, and S604. In the measurement phase, thesignal analysis block 114 starts receiving data again in S605. Ifrequired, S606 extracts patterns that are similar to the templateidentified in S603. S607 calculates feature values within the signalfrequency band only. In the calibration phase, it was verified that thefeature values of the data within the signal frequency band indicate theusage or condition of the target.

The output of S607 corresponds to the usage or condition of the target.If the output of S607 increases expectedly, there may be a problem inthe target. In such a case, the signal analysis block 114 may send analert to the other computer on the network 115. In contrast, if therefewer feature values than the expectation, then planned maintenance maybe postponed.

The measurement process of S605, S606, and S607 continues unless itreceives a request to stop from the other computer on the network 115.

The above system and signal processing method provides not onlycompression of sensor data through calculating the feature values of thetarget, but also identification of a signal frequency band containingthe signal developed by the target device. Such implementations reducethe computing resource requirement thanks to the disclosed signalprocessing method which compresses the high-sampling-rate data byutilizing the characteristic of the machine states transitions.

FIG. 6 illustrates an example block diagram of a local computer inaccordance with another example implementation. In a second exampleimplementation, a sensor housing 501 involves a sensing element 502, awideband data transmission path 104, a data acquisition device (DAQ)103, an envelope detector 106, a summarize and downsampling block 107, anarrow-band transmission path 108 a decomposer 109, a feature extractor110, a summarize and down-sampling block 111, and a switch 503.

The sensor housing 501 may be functional as a pure sensor. As itincludes the sensor element 502 and the following signal processingblocks, the switch 503 switches the data path so that a user or othercomputers on a network can get the raw data (output of the DAQ 103)directly if needed. On the other hand, since the all the functionalityof the first example implementation is also provided, other computers ona network can also obtain feature signals of the target with theappropriate compressions as well. The switch 503 is controllable by thecomputers on a network 115 and a manual switch equipped in the sensorhousing 501.

In an example implementation, the switch 503 realizes the bothfunctionality of a typical sensor and a vibration analysis. This givesthe sensor housing 501 flexible capability from the IoT system designperspective.

FIG. 7 illustrates an example block diagram of a local computer inaccordance with another example implementation. In a third exampleimplementation, in addition to the first example implementation, ademodulator 701, a low pass filter 702, summarize and down-samplingblock 703, and a switch 704 are equipped in the extraction block 105 asshown in FIG. 7. In addition, decomposer 109 uses FFT to decompose datainto multiple band-limited data since applying a filter bank generallyrequires more calculation resources than FFT. Before the transition tothe measurement phase, the extraction block 105 works in the same manneras the first example implementation.

In the measurement phase, the extraction block 105 starts using thedemodulator 701, the low pass filter 702, and the summarize anddown-sampling block 703, instead of using decomposer 109 with FFT. Suchimplementations facilitate extracting feature signals so that theextraction block 105 can provide a variety of feature values, such ascrest factor and kurtosis which requires time-series data to calculate.

In the third example implementation, the extraction block 105 uses FFTin the calibration phase and the demodulator 701 in the measurementphase respectively. Once the identification of the signal frequencyband, which indicates feature values increasing in accordance with ausage or condition of the machine, only one bandpass filter is neededfor passing the signal frequency band. Such example implementationseliminate the need for redundant computing resources. As a result, thethird example implementation is able to provide a variety of featurevalues in the measurement phase while keeping the reduced resourcerequirement in the calibration phase by placing the switch 704.

FIG. 8 illustrates an example flow chart of the IoT system analysis, inaccordance with an example implementation. Specifically, FIG. 8illustrates the flow chart for the signal analysis block 114, inaccordance with the third example implementation. In FIGS. 8, S605 andS607 in FIG. 5 is replaced with S801 and S802 respectively. S801collects the time-series demodulated signal instead of band-limited datacalculated by FFT. S802 calculates a variety of feature values from thetime-series demodulated signal and down-samples them at the summarizeand down-sampling block 703.

By providing a variety of feature values, the signal analysis block 114can provide a more accurate usage or condition of the target due tohaving more information than the signal analysis involving signal kindof feature signals. Such example implementations can be useful forhaving multiple inputs such as artificial neural networks to the signalanalysis block 114 or other further analysis.

Example implementations can thereby realize data compression withoutserious disruption of information when a usage or a condition of amechanical part inside a machine is monitored.

FIG. 9 illustrates an example computing environment with an examplecomputer device suitable for use in some example implementations, suchas a computer, a server, or an Internet of Things (IoT) deviceintegrated with a sensor configured to provide the sensor data asillustrated in FIG. 6. In example implementations, other apparatuses canalso be utilized that provide a common enclosure or apparatus housing toa system involving a processor 910 integrated with a sensor tofacilitate the desired physical implementation for an apparatusconfigured to manage a machine having a plurality of machine states.Computer device 905 in computing environment 900 can include one or moreprocessing units, cores, or processors 910, memory 915 (e.g., RAM, ROM,and/or the like), internal storage 920 (e.g., magnetic, optical, solidstate storage, and/or organic), and/or IO interface 925, any of whichcan be coupled on a communication mechanism or bus 930 for communicatinginformation or embedded in the computer device 905. IO interface 925 isalso configured to receive images from cameras or provide images toprojectors or displays, depending on the desired implementation.

Computer device 905 can be communicatively coupled to input/userinterface 935 and output device/interface 940. Either one or both ofinput/user interface 935 and output device/interface 940 can be a wiredor wireless interface and can be detachable. Input/user interface 935may include any device, component, sensor, or interface, physical orvirtual, that can be used to provide input (e.g., buttons, touch-screeninterface, keyboard, a pointing/cursor control, microphone, camera,braille, motion sensor, optical reader, and/or the like). Outputdevice/interface 940 may include a display, television, monitor,printer, speaker, braille, or the like. In some example implementations,input/user interface 935 and output device/interface 940 can be embeddedwith or physically coupled to the computer device 905. In other exampleimplementations, other computer devices may function as or provide thefunctions of input/user interface 935 and output device/interface 940for a computer device 905.

Examples of computer device 905 may include, but are not limited to,highly mobile devices (e.g., smartphones, devices in vehicles and othermachines, devices carried by humans and animals, and the like), mobiledevices (e.g., tablets, notebooks, laptops, personal computers, portabletelevisions, radios, and the like), and devices not designed formobility (e.g., desktop computers, other computers, information kiosks,televisions with one or more processors embedded therein and/or coupledthereto, radios, and the like).

Computer device 905 can be communicatively coupled (e.g., via IOinterface 925) to external storage 945 and network 950 for communicatingwith any number of networked components, devices, and systems, includingone or more computer devices of the same or different configuration.Computer device 905 or any connected computer device can be functioningas, providing services of, or referred to as a server, client, thinserver, general machine, special-purpose machine, or another label.

IO interface 925 can include, but is not limited to, wired and/orwireless interfaces using any communication or IO protocols or standards(e.g., Ethernet, 802.11x, Universal System Bus, WiMax, modem, a cellularnetwork protocol, and the like) for communicating information to and/orfrom at least all the connected components, devices, and network incomputing environment 900. Network 950 can be any network or combinationof networks (e.g., the Internet, local area network, wide area network,a telephonic network, a cellular network, satellite network, and thelike).

Computer device 905 can use and/or communicate using computer-usable orcomputer-readable media, including transitory media and non-transitorymedia. Transitory media include transmission media (e.g., metal cables,fiber optics), signals, carrier waves, and the like. Non-transitorymedia include magnetic media (e.g., disks and tapes), optical media(e.g., CD ROM, digital video disks, Blu-ray disks), solid state media(e.g., RAM, ROM, flash memory, solid-state storage), and othernon-volatile storage or memory.

Computer device 905 can be used to implement techniques, methods,applications, processes, or computer-executable instructions in someexample computing environments. Computer-executable instructions can beretrieved from transitory media, and stored on and retrieved fromnon-transitory media. The executable instructions can originate from oneor more of any programming, scripting, and machine languages (e.g., C,C++, C #, Java, Visual Basic, Python, Perl, JavaScript, and others).

Processor(s) 910 can execute under any operating system (OS) (notshown), in a native or virtual environment. One or more applications canbe deployed that include logic unit 960, application programminginterface (API) unit 965, input unit 970, output unit 975, andinter-unit communication mechanism 995 for the different units tocommunicate with each other, with the OS, and with other applications(not shown). The described units and elements can be varied in design,function, configuration, or implementation and are not limited to thedescriptions provided. Processor(s) 910 can be in the form of hardwareprocessors such as central processing units (CPUs) or in a combinationof hardware and software units.

In some example implementations, when information or an executioninstruction is received by API unit 965, it may be communicated to oneor more other units (e.g., logic unit 960, input unit 970, output unit975). In some instances, logic unit 960 may be configured to control theinformation flow among the units and direct the services provided by APIunit 965, input unit 970, output unit 975, in some exampleimplementations described above. For example, the flow of one or moreprocesses or implementations may be controlled by logic unit 960 aloneor in conjunction with API unit 965. The input unit 970 may beconfigured to obtain input for the calculations described in the exampleimplementations, and the output unit 975 may be configured to provideoutput based on the calculations described in example implementations.

Processor(s) 910 can be configured to decompose sensor data over aplurality of band-limited data to generate a decomposed sensor data asillustrated at 109 of FIGS. 1, 4, 6 and 7; extract an envelope from thesensor data as illustrated at 106 of FIGS. 1, 4, 6, and 7; executedownsampling on the extracted envelope and the decomposed sensor data togenerate a downsampled envelope and downsampled decomposed sensor dataas illustrated at 107 and 111 of FIGS. 1, 4, 6, and 7; and synchronizethe downsampled envelope with the downsampled decomposed sensor data asa data pair as illustrated at 112 of FIGS. 1, 4, 6 and 7. Depending onthe desired implementation, processor(s) 910 can be configured todecompose the sensor data through a Fast Fourier Transform (FFT) asdescribed herein. Through such example implementations, the calculationof the feature values into a decomposed signal before downsamplingallows the retention of the high-frequency-band signal of the sensoroutput. The downsampling facilitates the compression of data whichrelaxes the storage/bandwidth/processing requirements of the IoT systemin consideration of vibration monitoring. The calculation anddownsampling of the signal envelope retains information regarding thecondition of the machine following the signal processing. Thesynchronization of the results as a data pair allows for the envelopeand spectrum data to be synchronized.

Processor(s) 910 can be further configured to collect data pairs until apredetermined condition is met (e.g., the condition can involve, but isnot limited to, a specific desired timing or data amount, and so on);identify a signal frequency band of the sensor data from the downsampleddecomposed sensor data; and for generation of subsequent data pairs,determine feature values within the signal frequency band from thesubsequent data pairs as illustrated in FIG. 5. Depending on the desiredimplementation, the generation of the subsequent data pairs can beconducted through a demodulator and a low-pass filter configuredaccording to the identified signal frequency band as illustrated in FIG.7. The signal frequency band can be determined based on determining thefrequency having a signal value that satisfies a predeterminedcondition. The identified frequency band can thereby be stored intomemory 915 for future reference.

Processor(s) 910 can be further configured to collect data pairs until athreshold is met; identify a signal frequency band of the sensor datafrom the downsampled decomposed sensor data; and for generation ofsubsequent data pairs involving the downsampled envelope and downsampleddemodulated sensor data in time-series, determine feature values fromthe subsequent data pairs as illustrated in FIG. 8. Processor(s) 910 canbe configured to manage sensor data from a machine having a plurality ofmachine states, by decomposing sensor data into a plurality ofband-limited data to generate a decomposed sensor data as illustrated at109 of FIGS. 1, 4, 6 and 7; and executing downsampling on each of thedecomposed sensor data to generate downsampled decomposed sensor data asillustrated at 107 and 111 of FIGS. 1, 4, 6, and 7. Depending on thedesired implementation the downsampling cycle can be shorter than theduration of the each of the machine states, as illustrated at 109 ofFIG. 1.

Processor(s) 910 can be further configured to extract feature valuesfrom the each of the decomposed sensor data as illustrated at 110 ofFIG. 1. As described in FIG. 1, the downsampling can be conducted basedon the extracted feature values to generate the downsampled decomposedsensor data.

Some portions of the detailed description are presented in terms ofalgorithms and symbolic representations of operations within a computer.These algorithmic descriptions and symbolic representations are themeans used by those skilled in the data processing arts to convey theessence of their innovations to others skilled in the art. An algorithmis a series of defined steps leading to a desired end state or result.In example implementations, the steps carried out require physicalmanipulations of tangible quantities for achieving a tangible result.

Unless specifically stated otherwise, as apparent from the discussion,it is appreciated that throughout the description, discussions utilizingterms such as “processing,” “computing,” “calculating,” “determining,”“displaying,” or the like, can include the actions and processes of acomputer system or other information processing device that manipulatesand transforms data represented as physical (electronic) quantitieswithin the computer system's registers and memories into other datasimilarly represented as physical quantities within the computersystem's memories or registers or other information storage,transmission or display devices.

Example implementations may also relate to an apparatus for performingthe operations herein. This apparatus may be specially constructed forthe required purposes, or it may include one or more general-purposecomputers selectively activated or reconfigured by one or more computerprograms. Such computer programs may be stored in a computer readablemedium, such as a computer-readable storage medium or acomputer-readable signal medium. A computer-readable storage medium mayinvolve tangible mediums such as, but not limited to optical disks,magnetic disks, read-only memories, random access memories, solid statedevices and drives, or any other types of tangible or non-transitorymedia suitable for storing electronic information. A computer readablesignal medium may include mediums such as carrier waves. The algorithmsand displays presented herein are not inherently related to anyparticular computer or other apparatus. Computer programs can involvepure software implementations that involve instructions that perform theoperations of the desired implementation.

Various general-purpose systems may be used with programs and modules inaccordance with the examples herein, or it may prove convenient toconstruct a more specialized apparatus to perform desired method steps.In addition, the example implementations are not described withreference to any particular programming language. It will be appreciatedthat a variety of programming languages may be used to implement theteachings of the example implementations as described herein. Theinstructions of the programming language(s) may be executed by one ormore processing devices, e.g., central processing units (CPUs),processors, or controllers.

As is known in the art, the operations described above can be performedby hardware, software, or some combination of software and hardware.Various aspects of the example implementations may be implemented usingcircuits and logic devices (hardware), while other aspects may beimplemented using instructions stored on a machine-readable medium(software), which if executed by a processor, would cause the processorto perform a method to carry out implementations of the presentapplication. Further, some example implementations of the presentapplication may be performed solely in hardware, whereas other exampleimplementations may be performed solely in software. Moreover, thevarious functions described can be performed in a single unit, or can bespread across a number of components in any number of ways. Whenperformed by software, the methods may be executed by a processor, suchas a general purpose computer, based on instructions stored on acomputer-readable medium. If desired, the instructions can be stored onthe medium in a compressed and/or encrypted format.

Moreover, other implementations of the present application will beapparent to those skilled in the art from consideration of thespecification and practice of the teachings of the present application.Various aspects and/or components of the described exampleimplementations may be used singly or in any combination. It is intendedthat the specification and example implementations be considered asexamples only, with the true scope and spirit of the present applicationbeing indicated by the following claims.

What is claimed is:
 1. A method for managing sensor data from a machinehaving a plurality of machine states, the method comprising: decomposingsensor data into a plurality of band-limited data to generate adecomposed sensor data; and executing downsampling on each of thedecomposed sensor data to generate downsampled decomposed sensor data;wherein the downsampling cycle is shorter than a duration of the each ofthe machine states, and wherein the downsampled decomposed sensor datais implemented to identify a signal frequency band containing a signaldeveloped by the machine where the method further includes: extractingan envelope from the sensor data; executing downsampling on theextracted envelope to generate a downsampled envelope; synchronizing thedownsampled envelope with the downsampled decomposed sensor data as datapairs, collecting the data pairs until a predetermined condition is met;identifying, from the downsampled decomposed sensor data, the signalfrequency band having a signal value satisfying another predeterminedcondition, and storing the identified signal frequency band.
 2. Themethod of claim 1, further comprising: extracting feature values fromthe each of the decomposed sensor data, wherein the downsampling isconducted based on the extracted feature values to generate thedownsampled decomposed sensor data.
 3. The method of claim 1, furthercomprising: extracting feature values from each of the decomposed sensordata, wherein the downsampling is conducted based on the extractedfeature values to generate the downsampled decomposed sensor data, andwherein the extracting feature values is conducted from the decomposedsensor data of the identified signal frequency band.
 4. The method ofclaim 3, wherein generation of subsequent data is conducted through ademodulator and a low-pass filter configured according to the identifiedsignal frequency band.
 5. The method of claim 1, wherein the decomposingthe sensor data is conducted through a Fast Fourier Transform (FFT). 6.The method of claim 1, wherein the method is executed by a processorintegrated with a sensor configured to provide the sensor data with acommon enclosure.
 7. A non-transitory computer readable medium, storinginstructions for managing sensor data from a machine having a pluralityof machine states, the instructions comprising: decomposing sensor datainto a plurality of band-limited data to generate a decomposed sensordata; and executing downsampling on each of the decomposed sensor datato generate downsampled decomposed sensor data; wherein the downsamplingcycle is shorter than a duration of the each of the machine states,wherein the downsampled decomposed sensor data is implemented toidentify a signal frequency band containing a signal developed by themachine wherein the instructions further includes: extracting anenvelope from the sensor data; executing downsampling on the extractedenvelope to generate a downsampled envelope; and synchronizing thedownsampled envelope with the downsampled decomposed sensor data as datapairs collecting the data pairs until a predetermined condition is met;identifying, from the downsampled decomposed sensor data, the signalfrequency band having a signal value satisfying another predeterminedcondition; and storing the identified signal frequency band.
 8. Thenon-transitory computer readable medium of claim 7, the instructionsfurther comprising: extracting feature values from the each of thedecomposed sensor data, wherein the downsampling is conducted based onthe extracted feature values to generate the downsampled decomposedsensor data.
 9. The non-transitory computer readable medium of claim 7,the instructions further comprising: extracting feature values from eachof the decomposed sensor data; wherein the downsampling is conductedbased on the extracted feature values to generate the downsampleddecomposed sensor data, and wherein the extracting feature values isconducted from the decomposed sensor data of the identified signalfrequency band.
 10. The non-transitory computer readable medium of claim9, wherein generation of subsequent data is conducted through ademodulator and a low-pass filter configured according to the identifiedsignal frequency band.
 11. The non-transitory computer readable mediumof claim 7, wherein the decomposing the sensor data is conducted througha Fast Fourier Transform (FFT).
 12. The non-transitory computer readablemedium of claim 7, wherein the instructions are executed by a processorintegrated with a sensor configured to provide the sensor data with acommon enclosure.